Chun-Shih & Gamon Relationships Among Learn... RELATIONSHIPS AMONG LEARNING STRATEGIES, PATTERNS, STYLES, AND ACHIEVEMENT IN WEB-BASED COURSES

This study analyzed the relationships among student achievement, learning strategies, learning patterns, learning styles, and student characteristics. The population of this study included 99 students taking two Web-based courses offered by a land grant university in the United States. Seventy-four students (75%) completed a learning style test, an on-line questionnaire, and received grades by the end of the semester. The learning style test was the Group Embedded Figures Test (GEFT), which classified students as either field-dependent or field-independent. The on-line questionnaire consisted of two scales (learning strategy and learning pattern), with pilot-test reliabilities of .80 and .72, respectively. The students used most of the learning strategies to find important ideas from lectures and to memorize key words of important concepts. They seemed to be more interested in checking their grades than in communicating with the class and instructors via e-mail, discussion forum, or chat room. Learning strategy was the only significant factor that explained about one-fourth of student achievement measured by class grade. Introduction and Theoretical Framework Evaluations of new educational technologies have tended to compare learning outcomes of instructional delivery methods with the hope that the new technology “will be the one to revolutionize learning” (Parson, 1998, p. 2). However, results of these evaluations are often disappointing. In a study that compiled 50 years of research comparing different delivery methods of instruction, Russell (1998) found no significant differences in learning outcomes, when looking solely at the medium of delivering instruction. Moreover, Clark (1983, p. 445) stressed, “media are mere vehicles that deliver instruction but do not influence student achievements any more than the truck that delivers our groceries causes changes in nutrition.” A number of reviews on distance education research have accepted Clark’s arguments (Willis, 1993; Moore & Kearsley, 1996; Sherry, 1996; & Hanson, Maushak, Schlosser, Anderson, Sorenson, & Simonson, 1997). In essence, Alexander (1995) argued that questions about application of new technologies should not be in terms of media such as print, video, computer, or oral traditions. The most important question should be: What is known about the way students learn via the new technology? The latest in the long line of technologies is the Internet/World Wide Web (WWW). As the popularity of the Internet/WWW increases, its use as a means of delivering instruction is also growing. Alexander (1995, p. 3) believed that “the greatest potential of the Web, however, lies in the fact that we have a chance to learn from the lessons of the previous faded technologies, and an opportunity to develop new learning experiences for students that have not been possible before.” However, Parson (1998) and Alexander (1995) argued that while implementing a new technology, educators should evaluate how students learn via the new technology so as to help with curriculum and instructional designs. Moreover, Parson (1998) stressed the importance of understanding how the new technology can affect learning when it is used by different types of learners. Journal of Agricultural Education 1 Volume 43, Number 4, 2002 Chun-Shih & Gamon Relationships Among Learn... Identifying student learning styles helps educators understand how people perceive and process information in different ways. Garger and Guild (1984, p.11) described learning styles as “stable and pervasive characteristics of an individual, expressed through the interaction of one’s behavior and personality as one approaches a learning task.” According to Cano, Garton, and Raven (1992), one of the most widely studied learning style theories contrasts field-dependence and field-independence. The Group Embedded Figure Test (GEFT), a standardized cognitive test, can be administered to determine the preferred learning styles of learners as either fielddependent or field-independent (Oltman, Raskin, & Witkin, 1971). Literature on learning styles suggests that field-dependent learners tend to approach a problem in a more global way, are socially oriented, prefer collaboration, and are extrinsically motivated (Miller, 1997a; Raven, Cano, Garton, & Shellhamer, 1993; Witkin, Moore, Goodenough, & Cox, 1977). In contrast, field-independent learners tend to approach a problem more analytically, rely on self-structured situations, prefer competition, and are intrinsically motivated. Garger and Guild (1984) emphasized that both field-dependent and fieldindependent people make equally good learners. Yet, since learning styles affect how successfully people learn in specific situations, educators should be sensitive to learning style differences (Garger & Guild, 1984). Several studies have shown that field-independent learners tend to outperform field-dependent learners in various settings (Annis, 1979; Moore & Dwyer, 1992; Ronning, McCurdy, & Ballinger, 1984). However, in their study related to the effects of learning styles on achievement in a WWW course, Day, Raven, and Newman (1997) found learning styles had no effect on student achievement or attitude in Web-based instruction, which echoes the findings of the study on learning styles in a hypermedia environment conducted by Liu and Reed (1994). Similar to the literature on learning styles, literature on learning strategies explores different ways of learning (Pintrich & Johnson, 1990; Cross & Steadman, 1996; Weinstein & Meyer, 1991). In assuming stability as well as lack of individual control, literature on learning style suggests that it may be difficult for students to change their learning styles (Pintrich & Johnson, 1990). However, literature on learning strategy assumes that students’ motivation and use of learning strategies can be controlled by learners and changed through teaching. According to Cross and Steadman (1996), cognitive learning strategies are behavioral skills learners can use to improve their understanding, integration, and retention of new information. Learning strategies include a wide variety of cognitive processes and behavioral skills (Weinstein & Meyer, 1991). General learning strategy components include rehearsal, elaboration, organization, comprehension, metacognition, and resource management (Cross & Steadman, 1996; Weinstein & Meyer, 1991). Pintrich, Smith, Garcia, and McKeachie (1991) developed a learning strategy instrument, Motivation Strategies for Learning Questionnaire (MSLQ). This instrument includes two main sections: one on motivation and one on learning strategies. The learning strategies section consists of two components and eight scales. The two components were: cognitive and metacognitive strategies, and resource management strategies; the eight scales were: rehearsal, elaboration, organization, critical thinking, metacognitive selfregulation, time and study environment, effort regulation, and help seeking (Pintrich et al., 1991). Miller (1997b) identified 12 learning strategies used by the students studying agriculture through videotapes. Pausing the tape while viewing and taking notes was the learning strategy that the students used most. Miller (1997b, p. 21) defined learning strategies as “the techniques or skills used by an individual in accomplishing a learning task.” His definition is different and not as broad as the definition in Mayer’s study (1988). Mayer (1988, p. 11) defined learning strategies as “behaviors of a learner that are intended to manipulate a person’s cognitive processes during learning.” Journal of Agricultural Education 2 Volume 43, Number 4, 2002 Chun-Shih & Gamon Relationships Among Learn... In their study on relationships between learning strategies and learning styles in a hypermedia environment, Liu and Reed (1994) used the term “learning patterns” in discussing learning strategies. In Liu and Reed’s study, learning patterns were measured by identifying how often the students accessed different functions in a hypermedia environment and how long the students used the courseware, which seems to be quite similar to Miller’s (1997b) definition of learning strategies. Liu and Reed (1994) found that different learning style groups employed different patterns of learning in completing the same task. What do we know about the way students learn via the new technology, such as the Internet/WWW? What are the important learning factors in Web-based courses? Do student learning styles, learning strategies, and patterns of learning influence their learning achievement? Research is needed to obtain more understanding of the learning factors that influence student success in Web-based learning. Moreover, research is needed to understand student learning strategies and patterns of learning with different learning styles via the Internet/WWW. This type of research will assist educators in planning, organizing, and delivering quality Webbased instruction in a manner that will improve student learning. Purpose and Objectives This study was a formative evaluation designed to enhance teaching and learning. Its purpose was to study how students with different learning styles learned in Webbased courses that were offered through the College of Agriculture at a land grant university in United States, and to determine what factors influenced their learning. The objectives of the study were to identify: a) Differences in student learning strategies, patterns of learning, and achievement in relation to learning styles, and b) Relationships between student achievement and selected variables (student learning strategies, learning patterns, learning styles, and student characteristics). Methods and Procedures The population of this study included 99 students taking two non-major biology introductory courses, Zoology 155 and Biology 109. These two Web-based courses were stand-alone courses in which most course materials and resources were accessed and delivered by the Internet. More than 60% of the population was oncampus students and almost 40% was offcampus students. Thirty-two out of the 39 off-campus students were high school students. Before the study was conducted, a letter was sent to the high school teachers to seek permission for their students to participate in this study. The Group Embedded Figures Test (GEFT) was used to determine preferred learning styles, either as field-dependent (FD) or field-independent (FI). Individuals scoring greater than the national mean (11.4) were classified as field-independent learners, whereas those scoring less than the national mean were considered to prefer a field-dependent style. The total possible raw score on the GEFT was 18. The reliability coefficient for the GEFT was .82 (Witkin, Oltman, Raskin, & Karp, 1971). The researchers designed an on-line questionnaire, which included two scales and demographic questions. The questionnaire, written in HTML (HyperText Markup Language) format, was posted on the web. Based on their relevance to the nature of the two Web-based courses studied, 13 statements representing the learning strategies scale were selected from the Motivation Strategies for Learning Questionnaire (MSLQ) (Pintrich et al., 1991). The students were asked to indicate the extent to which the statements described them while they were taking the Web-based course. The response options of the scale ranged from (1) Not at all typical of me to (5) Very much typical of me. As to the learning patterns scale, it consisted of 15 statements based on the techniques or interactive functions in the Web-based courses that students used to accomplish a task. It was a five-point scale with response Journal of Agricultural Education 3 Volume 43, Number 4, 2002 Chun-Shih & Gamon Relationships Among Learn... options ranging from (1) None of the time to (5) All of the time. Demographic variables included courses previously taken in the subject area, study and work hours per week, class level, and gender. Content and face validity for the questionnaire were established by a panel of three faculty members involved in developing the two Web-based courses of this study and three graduate students in Agricultural Education. The two scales were pilot-tested for reliability with 38 students taking a Web-based course, Biology 201. Cronbach’s alpha coefficients were .80 and .72 for the learning strategies, and patterns of learning scales, respectively. Students were asked to complete an online questionnaire three weeks before the final exams. A follow-up electronic letter to the non-respondents of the on-line questionnaire yielded a total of 94 responses for a 95% return rate. The researchers administered the learning style test (GEFT) to on-campus students and proctors administered it to off-campus students. A total of 78 students (79%) completed the GEFT. Instructors provided grades for all students at the end of the semester, and these were used as a measure of achievement. For purposes of analysis, the learning style scores, questionnaire responses, and student grades were matched. This yielded a final response rate of 75% (74 out of the 99 students), which was considered to be an acceptable representation of the population. Data were analyzed using the Statistical Package for Social Science, Personal Computer Version (SPSSx/PC). Analyses of data included frequencies, means, standard deviations, t-tests, Pearson correlations, and regressions. The alpha level was established a priori at the .05.